Current state-of-the-art methodologies in structural health monitoring and damage detection rely heavily on the use of baseline data collected from the structure in the undamaged state. The methodologies are based on comparing the current sensor response signal data to the previously recorded baseline sensor response signal data, and using the differences to glean information about structural damage therefrom. It is known that environmental effects (such as temperature) can cause changes in the recorded signals, including baseline data, which in turn, will adversely affect most damage detection and evaluation schemes. However, a prior set of baseline data may not be readily obtainable, or environmental sensors may not be able to function under all operational conditions. Therefore, to overcome this difficulty, it is desirable to have a method for dynamically compensating sensor response signal data for the effects of environmental variables, such as temperature or other environmental variables, using sparse data acquired during the operation of a SHM system without the need to measure the environmental variables.